Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
GWAS SNP Interpretation Skill
Overview
Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context.
Use Cases:
"Interpret rs7903146" (TCF7L2 diabetes variant)
"What diseases is rs429358 associated with?" (APOE Alzheimer's variant)
"Clinical significance of rs1801133" (MTHFR variant)
"Is rs12913832 in any fine-mapped loci?" (Eye color variant)
What It Does
The skill provides a comprehensive interpretation of SNPs by:
SNP Annotation : Retrieves basic variant information including genomic coordinates, alleles, functional consequence, and mapped genesAssociation Discovery : Finds all GWAS trait/disease associations with statistical significanceFine-Mapping Evidence : Identifies credible sets the variant belongs to (fine-mapped causal loci)Gene Mapping : Uses Locus-to-Gene (L2G) predictions to identify likely causal genesClinical Summary : Aggregates evidence into actionable clinical significance
Workflow
User Input: rs7903146
↓
[1] SNP Lookup
→ Get location, consequence, MAF
→ gwas_get_snp_by_id
↓
[2] Association Search
→ Find all trait/disease associations
→ gwas_get_associations_for_snp
↓
[3] Fine-Mapping (Optional)
→ Get credible set membership
→ OpenTargets_get_variant_credible_sets
↓
[4] Gene Predictions
→ Extract L2G scores for causal genes
→ (embedded in credible sets)
↓
[5] Clinical Summary
→ Aggregate evidence
→ Identify key traits and genes
↓
Output: Comprehensive Interpretation Report
Data Sources
GWAS Catalog (EMBL-EBI)
SNP annotations : Functional consequences, mapped genes, population frequenciesAssociations : P-values, effect sizes, study metadata
Coverage : 350,000+ publications, 670,000+ associations
Open Targets Genetics
Fine-mapping : Statistical credible sets from SuSiE, FINEMAP methodsL2G predictions : Machine learning-based gene prioritizationColocalization : QTL evidence for causal genesCoverage : UK Biobank, FinnGen, and other large cohorts
Input Parameters
Required
rs_id (str): dbSNP rs identifier
Format: "rs" + number (e.g., "rs7903146")
Must be valid rsID in GWAS Catalog
Optional
include_credible_sets (bool, default=True): Query fine-mapping data
True: Complete interpretation (slower, ~10-30s)
False: Fast associations only (~2-5s)
p_threshold (float, default=5e-8): Genome-wide significance thresholdmax_associations (int, default=100): Maximum associations to retrieve
Output Format Returns SNPInterpretationReport containing:
1. SNP Basic Info {
'rs_id': 'rs7903146',
'chromosome': '10',
'position': 112998590,
'ref_allele': 'C',
'alt_allele': 'T',
'consequence': 'intron_variant',
'mapped_genes': ['TCF7L2'],
'maf': 0.293
}
2. Trait Associations [
{
'trait': 'Type 2 diabetes',
'p_value': 1.2e-128,
'beta': '0.28 unit increase',
'study_id': 'GCST010555',
'pubmed_id': '33536258',
'effect_allele': 'T'
},
...
]
3. Credible Sets (Fine-Mapping) [
{
'study_id': 'GCST90476118',
'trait': 'Renal failure',
'finemapping_method': 'SuSiE-inf',
'p_value': 3.5e-42,
'predicted_genes': [
{'gene': 'TCF7L2', 'score': 0.863}
],
'region': '10:112950000-113050000'
},
...
]
4. Clinical Significance Genome-wide significant associations with 100 traits/diseases:
- Type 2 diabetes
- Diabetic retinopathy
- HbA1c levels
...
Identified in 20 fine-mapped loci.
Predicted causal genes: TCF7L2
Example Usage See QUICK_START.md for platform-specific examples.
Tools Used
GWAS Catalog Tools
gwas_get_snp_by_id: Get SNP annotationgwas_get_associations_for_snp: Get all trait associations
Open Targets Tools
OpenTargets_get_variant_info: Get variant details with population frequenciesOpenTargets_get_variant_credible_sets: Get fine-mapping credible sets with L2G
Interpretation Guide
P-value Significance Levels
p < 5e-8 : Genome-wide significant (strong evidence)p < 5e-6 : Suggestive (moderate evidence)p < 0.05 : Nominal (weak evidence)
L2G Score Interpretation
> 0.5 : High confidence causal gene0.1-0.5 : Moderate confidence< 0.1 : Low confidence
Clinical Actionability
High : Multiple genome-wide significant associations + in credible sets + high L2G scoresModerate : Genome-wide significant associations but limited fine-mappingLow : Suggestive associations or limited replication
Limitations
Variant ID Conversion : OpenTargets requires chr_pos_ref_alt format, which may need allele lookupPopulation Specificity : Associations may vary by ancestryEffect Sizes : Beta values are study-dependent (different phenotype scales)Causality : Associations don't prove causation; fine-mapping improves confidenceCurrency : Data reflects published GWAS; latest studies may not be included
Best Practices
Use Full Interpretation : Enable include_credible_sets=True for clinical decisionsCheck Multiple Variants : Look at other variants in the same locusValidate Populations : Consider ancestry-specific effect sizesReview Publications : Check original studies for contextIntegrate Evidence : Combine with functional data, eQTLs, pQTLs
Technical Notes
Performance
Fast mode (no credible sets): 2-5 secondsFull mode (with credible sets): 10-30 secondsBottleneck : OpenTargets GraphQL API rate limits
Error Handling
Invalid rs_id: Returns error message
No associations: Returns empty list with note
API failures: Graceful degradation (returns partial results)
Related Skills
Gene Function Analysis : Interpret predicted causal genesDisease Ontology Lookup : Understand trait classificationsPubMed Literature Search : Find original GWAS publicationsVariant Effect Prediction : Functional consequence analysis
References
Version
Version : 1.0.0Last Updated : 2026-02-13ToolUniverse Version : >= 1.0.0Tools Required : gwas_get_snp_by_id, gwas_get_associations_for_snp, OpenTargets_get_variant_credible_sets 